Search Results for author:
Found 15 papers, 6 papers with code
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
End-to-End Open-Domain Question Answering with BERTserini
We demonstrate an end-to-end question answering system that integrates BERT with the open-source Anserini information retrieval toolkit.
Ranked #4 on
Open-Domain Question Answering
on SQuAD1.1 dev
Segatron: Segment-Aware Transformer for Language Modeling and Understanding
To verify this, we propose a segment-aware Transformer (Segatron), by replacing the original token position encoding with a combined position encoding of paragraph, sentence, and token.
Ranked #20 on
Language Modelling
on WikiText-103
LLM-Powered Hierarchical Language Agent for Real-time Human-AI Coordination
AI agents powered by Large Language Models (LLMs) have made significant advances, enabling them to assist humans in diverse complex tasks and leading to a revolution in human-AI coordination.
Rapid Adaptation of BERT for Information Extraction on Domain-Specific Business Documents
Techniques for automatically extracting important content elements from business documents such as contracts, statements, and filings have the potential to make business operations more efficient.
Evaluating Token-Level and Passage-Level Dense Retrieval Models for Math Information Retrieval
With the recent success of dense retrieval methods based on bi-encoders, studies have applied this approach to various interesting downstream retrieval tasks with good efficiency and in-domain effectiveness.
Ranked #1 on
Math Information Retrieval
on ARQMath
(using extra training data)
Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering
Recently, a simple combination of passage retrieval using off-the-shelf IR techniques and a BERT reader was found to be very effective for question answering directly on Wikipedia, yielding a large improvement over the previous state of the art on a standard benchmark dataset.
Ranked #3 on
Open-Domain Question Answering
on SQuAD1.1 dev
Multi-Kernel Correntropy for Robust Learning
In a recent work, the concept of mixture correntropy (MC) was proposed to improve the learning performance, where the kernel function is a mixture Gaussian kernel, namely a linear combination of several zero-mean Gaussian kernels with different widths.
Asymmetric Correntropy for Robust Adaptive Filtering
Correntropy is generally defined as the expectation of a Gaussian kernel between two random variables.
Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates
Behavior of deep neural networks can be inconsistent between different versions.
An Embedding-Based Grocery Search Model at Instacart
To train the model efficiently on noisy data, we propose a self-adversarial learning method and a cascade training method.
Improving Prediction Backward-Compatiblility in NLP Model Upgrade with Gated Fusion
When upgrading neural models to a newer version, new errors that were not encountered in the legacy version can be introduced, known as regression errors.
Gate Recurrent Unit Network based on Hilbert-Schmidt Independence Criterion for State-of-Health Estimation
State-of-health (SOH) estimation is a key step in ensuring the safe and reliable operation of batteries.
Approximating Human-Like Few-shot Learning with GPT-based Compression
Leveraging the approximated information distance, our method allows the direct application of GPT models in quantitative text similarity measurements.
